Intra code-rate predicting method in video coding

ABSTRACT

Embodiments of the present disclosure disclose a quick intra code-rate predicting method in the field of video coding, which may skip an entropy coding procedure during an RDO process by modeling residual information of a prediction block and predicting the number of coding bits of the prediction block based on an information entropy theory under a corresponding model. The code-rate predicting method comprises: making statistics on prediction block distribution information and modeling to obtain a combined model, predicting the number of coding bits of a prediction mode based on the model, and correcting the predicted number of coding bits for predicting the code-rate of each prediction mode during the RDO process, so as to replace a high time complexity in an actual entropy coding procedure, thereby effectively reducing the coding time with less video quality loss. The present disclosure is applicable to Iframe code-rate prediction for in video coding.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a national stage filing under 35 U.S.C. § 371of PCT/CN2017/094015, filed on Jul. 24, 2017 which claims priority to CNApplication No. 201610948219.3 filed on Oct. 26, 2016. The applicationsare incorporated herein by reference in their entirety.

FIELD

The present disclosure generally relates to the field of video coding,specifically relates to an intra (IFrame) prediction and codingtechnology, and more particularly relates to an intra (IFrame) code-ratepredicting method in video coding, which may quickly and efficientlyselect a best mode for intra prediction and thus reduce the time neededfor intra coding.

BACKGROUND

Video coding is a technology of video compression. Video data in dailylife include two parts: information and redundant data. Video codingaims to remove the redundant part of the video data to alleviate theburden of storing and transmitting video data. Current mainstream videocoding platforms adopt a block-based combined coding framework, where ablock needs to go through the procedures of predicting, transforming,quantizing, and entropy coding so as to eliminate statisticalredundancies in the video data as much as possible.

RDO (Rate-Distortion Optimization) is a scheme of reducing the number ofcoding bits as much as possible while guaranteeing a certain videoquality or a scheme of reducing coding distortion as much as possibleunder restriction of a certain code rate. During a rate distortionprocess, merits of each prediction approach is evaluated using arate-distortion function, mainly by means of evaluating the distortionand the number of coding bits of each prediction mode. Under a samedistortion, a prediction mode with a smaller number of coding bits isselected during the RDO process. Therefore, the distortion and thenumber of coding bits of each prediction mode need to be obtained duringthe RDO process. However, to obtain the number of coding bits of acertain prediction mode, entropy coding needs to be performed to aresult of quantizing a transform of the prediction block, which is arather time-consuming process.

SUMMARY

To overcome the drawbacks in the existing technologies, the presentdisclosure provides an intra code-rate predicting method in videocoding, which skips an entropy coding procedure mainly by means ofmodeling information of a prediction block and predicting the number ofcoding bits of the prediction block using an information entropy theoryunder the corresponding model; the method according to the presentdisclosure may quickly and efficiently select a best mode for intraprediction, thereby reducing the time needed for intra coding. In thisway, with less video quality loss (about 0.64% BD-rate), more time forcoding is saved (37.7% RDO module time).

During an RDO process, an optimal prediction mode under each kind ofblock partitioning is first selected; and then a best block partitioningmode is selected out of the optimal modes of various kinds of blockpartitioning. A key innovative point of the present disclosure lies inthat: the present disclosure adopts a novel model that combinesgeneralized Gaussian distribution and uniform distribution; based on thenovel model, fitting of residual distributions in the video coding iscloser to an actual result; besides, because a tail of the model isrepresented by uniform form, a computing complexity of the procedure ofupdating a lookup table may be greatly reduced during the entire coderate prediction process. The method according to the present disclosureadds a procedure of correcting the predicted number of coding bits,which is equivalent to correcting a decision result of the combinedmodel, such that the corrected result is closer to an actual entropycoding result.

A technical solution of the present disclosure is provided below:

A intra code-rate predicting method in video coding, which may skip anentropy coding procedure during an RDO (Rate-Distribution Optimization)process and effectively reduce the time for coding by means of modelingresidual information of a prediction block and predicting the number ofcoding bits of the prediction block based on an information entropytheory under the corresponding model; the code-rate predicting methodmainly comprises: a procedure of making statistics on prediction blockdistribution information and modeling, a procedure of predicting thenumber of coding bits in a prediction mode based on the model, and aprocedure of correcting the predicted number of coding bits; specificsteps of the method are provided below:

1) the procedure of making statistics on prediction block distributioninformation and modeling comprises:

11) making statistics, in a unit of one frame, on distribution ofresiduals at each position in different prediction block sizes (duringthe RDO procedure, the prediction block is partitioned into differentsizes, from 4×4 to 64×64), and fitting the residuals at each position inthe prediction block using a combined model that combines generalizedGaussian distribution and uniform distribution model, wherein aresulting probability density function is used as a combined model of aprobability distribution at the each position in the prediction block.(Note: an updating frequency of the combined model is once per frame inprinciple, i.e., the statistical result of the current frame is used inprediction of a next frame. However, if a certain number of samples isnot reached at the end of one frame, the model will not be updated;instead, the statistical sample is updated together with the sample ofthe next frame).

12) computing, with the model obtained in step 11), a probability of theprediction block at the each position with a quantization result to be aspecific value (i.e., supposing a quantization value obtained afterquantization of a certain position on a certain prediction block is x,solving the probability of occurrence for such an occasion), and thensolving an information entropy based on the probability. The resultinginformation entropy is used as the predicted number of coding bits ofthe prediction block at this position with this quantization value. Thepredicted number of coding bits is saved in a data table, such that thecorresponding number of coding bits may be obtained only by looking upthe data table during the ROD process for coding the next frame.

2) predicting the number of coding bits of each prediction mode in theRDO process based on the model:

The predicted number of coding bits of the prediction block at eachposition with each quantization value has been obtained during themodeling process; after the procedures of predicting, transforming, andquantizing, the predicted number of bits is obtained by looking up thecorresponding data table based on the quantization result at eachposition, and the predicted number of coding bits of the predictionblock may be obtained by summing up the numbers of coding bits at allpositions of the prediction block; in this way, the procedure of entropycoding is skipped.

3) correcting the predicted number of coding bits:

With the result of step 2), an optimal prediction mode in each kind ofblock size (4×4≠64×64) is selected while skipping the procedure ofentropy coding (this has been implemented during the RDO process; in thepresent disclosure, step 2) is used to replace the entropy codingprocedure that has a relatively high computational complexity during theRDO process). Immediately next to the ROD process, a best mode issingled out as the final partitioning result from among the optimalmodes in different block partitioning. In the present disclosure, afterthe optimal mode for each block size is obtained, a simple entropycoding is performed for these optimal modes, and the simple entropycoding result is used as the predicted number of coding bits in theoptimal mode, to be used for singling out a best partitioning modebetween different block sizes.

Compared with the prior art, the present disclosure has the followingbeneficial effects:

The present disclosure provides a intra code-rate predicting method invideo coding, which skips an entropy coding procedure mainly by means ofmodeling information of a prediction block and predicting the number ofcoding bits of the prediction block using an information entropy theoryunder a corresponding model; the method according to the presentdisclosure may quickly and efficiently select a best mode for intraprediction, thereby reducing the time needed for intra coding. Thepresent disclosure has the following advantages:

(1) a combined model that combines generalized Gaussian distribution anduniform distribution is more in conformity with the actual residualdistribution, such that an optimal prediction mode may be identified inthe same kind of block size.

(2) an entropy coding (a simple entropy coding) is re-done for theoptimal mode in each kind of block size; compared with the actualentropy coding, this procedure has a lower complexity, but suffices tobe used for deciding a best block partitioning approach, with less videoquality loss (0.64% BD-rate loss).

(3) the statistical model is updated once per frame, while in the RDOprocess, it is only needed to perform a simple lookup operation, suchthat the time complexity is relatively low (saving 37.7% RDO moduletime).

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow block diagram of a code-rate predicting method providedaccording to the present disclosure.

FIG. 2 shows a probability density function diagram of the existinggeneralized Gaussian distribution model and the combined model providedby the present disclosure;

where the dotted-line curve represents a traditional generalizedGaussian distribution model; the solid-line curve represents a combinedmodel that combines the generalized Gaussian distribution and theuniform distribution; and intersected points between two vertical linesand the transverse axis represent boundary points between thegeneralized Gaussian distribution and the uniform distribution in thecombined model.

FIG. 3 shows a schematic diagram of computing a probability ofquantizing a certain position in a prediction block to a specific valuein the present disclosure;

in the figure, the curve represents a residual distribution at a certainposition in the prediction block; the transverse axis represents a valueof the position before quantization, and the longitudinal axisrepresents a probability density of assuming a specific value for theposition before quantization; for this position, in the case of thequantized result {circumflex over (x)} ({circumflex over (x)}≠0), thequantization equation

$\left( {{\hat{x} = {{int}\mspace{11mu} {\left( \frac{{x} + {f \cdot Q_{step}}}{Q_{step}} \right) \cdot {sgn}}\mspace{11mu} (x)}},} \right.$

where f denotes quantization offset, Q_(step) denotes quantization stepsize, int(x) denotes a rounding function, and sgn(x) denotes a symbolfunction) shows that the corresponding value before quantization may belocated in an interval ((|{circumflex over (x)}|−f)·Q_(step),(|{circumflex over (x)}|+1−f)·Q_(step)), such that by evaluating theintegral of the curve in this interval, a probability of quantizing theposition to {circumflex over (x)} ({circumflex over (x)}≠0) may beobtained, the area of the black shadow part in FIG. 3; for the quantizedresult {circumflex over (x)} ({circumflex over (x)}=0), it may beobtained that the interval before quantization may be located(−(1−f)·Q_(step), (1−f)·Q_(step)), which interval is referred to as aquantization deadzone; the area of the white shadow part in the middleportion of FIG. 3 represents a probability of quantizing the position to{circumflex over (x)} ({circumflex over (x)}=0).

DETAILED DESCRIPTION OF EMBODIMENTS

Hereinafter, the present disclosure is further described through theembodiments, but the scope of the present disclosure is not limited inany manner.

The present disclosure provides a quick code-rate prediction method foran RDO (Rate Distribution Optimization) module in IFrame coding in thefield of video coding, for predicting the code rate of coding in eachprediction mode during the RDO process so as to replace the huge timecomplexity during an actual entropy coding procedure, which saves muchcoding time (37.7% RDO module time) with less video quality loss (0.64%BD-rate loss), The present disclosure is applicable to code-rateprediction for IFrame in video coding.

The present disclosure may skip an entropy coding procedure during anRDO process and effectively reduce the coding time by means of modelingresidual information of a prediction block and predicting the number ofcoding bits of the prediction block based on an information entropytheory tinder a corresponding model; FIG. 1 shows a flow block diagramof a code-rate predicting method provided according to the presentdisclosure, mainly comprising: making statistics on prediction blockdistribution information and modeling, predicting the number of codingbits of a prediction mode based on the model, and correcting thepredicted number of coding bits; specific steps of the method areprovided below:

1) the procedure of making statistics on prediction block distributioninformation and modeling comprises:

11) making statistics, in a unit of one frame, on distribution ofresiduals at each position in different prediction block sizes (from 4×4to 64×64), and fitting the residuals at each position in the predictionblock using a combined model that combines generalized Gaussiandistribution and uniform distribution model, thereby obtaining aprobability distribution at each position in the prediction block,

wherein: an updating frequency of the model is once per frame inprinciple, i.e., the statistical result of the current frame is used inprediction of a next frame. However, if a certain number of samples isnot reached at the end of one frame, the model will not be updated;instead, the statistical sample is updated together with the sample ofthe next frame.

12) computing a probability of a quantization result of the predictionblock at each position to be a specific value, and then solving aninformation entropy using an information entropy equation(f(P)=−log(P)). The resulting information entropy is used as thepredicted number of coding bits of the prediction block at this positionwith this quantization value. The predicted number of coding bits issaved in a data table, such that the corresponding number of coding bitsmay be obtained only by looking up the data table during the ROD processfor coding the next frame.

2) predicting the number of coding bits of the prediction mode based onthe model:

The predicted number of coding bits of the prediction block at eachposition with each quantization value has been obtained during themodeling process; after the procedures of predicting, transforming, andquantizing, the predicted number of bits is obtained by looking up thecorresponding data table based on the quantization result at eachposition, and the predicted number of coding bits of the predictionblock may be obtained by summing up the results; in this way, theprocedure of entropy coding is skipped.

3) correcting the predicted number of coding bits:

An optimal prediction mode of the current block size is decidedaccording to the approach above in each block size (4×4 64×64). Afterthe optimal modes for all block sizes are obtained, a simple entropycoding will be performed for these optimal modes, and a result of thesimple entropy coding is used as the predicted number of coding bits forthe best mode, to be used for singling out a best partitioning modebetween different block sizes.

During the procedure of making statistics on distribution information ofthe prediction block and modeling, specifically: statistics is made, ina unit of one frame, on the residual distribution at each position indifferent prediction block sizes, wherein each position has anindependent residual distribution model, and the residuals at the eachposition in the prediction block are fitted using a combined model thatcombines generalized Gaussian distribution and uniform distributionmodel, thereby obtaining a probability distribution at the each positionin the prediction block. A function expression of the generated Gaussiandistribution is provided below:

$\begin{matrix}{{{{f_{uv}(x)} = {\frac{\eta_{uv}{\alpha_{uv}\left( \eta_{uv} \right)}}{2\sigma_{uv}{\Gamma \left( {1/\eta_{uv}} \right)}}\exp \left\{ {- \left\lbrack {{\alpha_{uv}\left( \eta_{uv} \right)}\frac{x}{\sigma_{uv}}} \right\rbrack^{\eta_{uv}}} \right\}}};}{{wherein}\text{:}}} & \left( {{equation}\mspace{14mu} 1} \right) \\{{{\alpha_{uv}\left( \eta_{uv} \right)} = \sqrt{\frac{\Gamma \left( {3/\eta_{uv}} \right)}{\Gamma \left( {1/\eta_{uv}} \right)}}};} & \left( {{equation}\mspace{14mu} 2} \right)\end{matrix}$

In the expression, f_(uv)(x) represents a probability densitydistribution (where u and v are coordinates of a position in theprediction block) of each position in the prediction block, σ_(uv)denotes a standard deviation at the position, η_(uv) controls a shape ofthe probability density function (predicted using the expression

$\left. {\eta_{uv} = {{0.2718/\left( {0.7697 - \frac{E^{2}\left\{ {x} \right\}}{E\left\{ x^{2} \right\}}} \right)} - 0.1247}} \right),$

and Γ(⋅) represents a gamma function. In the present disclosure, thegeneralized Gaussian distribution model and the uniform distributionmodel are combined into a combined model, whose f_(uv)′(x) is shown inequation 3:

$\begin{matrix}{{f_{uv}^{\prime}(x)} = \left\{ {\begin{matrix}{{{\theta_{uv} \cdot {f_{uv}(x)}}{x}} \leq b_{uv}} \\{{{f_{uv}^{\prime}\left( b_{uv} \right)}b_{uv}} < {x} \leq m_{uv}} \\\begin{matrix}{0\mspace{34mu}} & {{x} \geq m_{uv}}\end{matrix}\end{matrix};} \right.} & \left( {{equation}\mspace{14mu} 3} \right)\end{matrix}$

where θ_(uv) is an adjustment factor to guarantee that the integral ofthe probability density function is 1 in the entire interval; b_(uv)represents a boundary between the generalized Gaussian distribution andthe uniform distribution; m_(uv) represents an available maximum valueof x after quantization, thereby obtaining an expression of the combinedmodel that combines the generalized Gaussian distribution and theuniform distribution; as shown in FIG. 2. FIG. 2 shows a probabilitydensity function diagram of the existing generalized Gaussiandistribution model and the combined model provided by the presentdisclosure.

During the procedure of predicting the number of coding bits of theprediction mode based on the model, specifically, a probability (P{

={circumflex over (x)}}) of the quantization result to be a certainspecific value {circumflex over (x)} is first computed based on theprobability density function of the combined model. FIG. 3 shows aschematic diagram of computing a probability of quantizing a certainposition in a prediction block to a specific value in the presentdisclosure. As shown in FIG. 3, a computation expression for thegeneralized Gaussian distribution in the model is represented inequation 4:

$\begin{matrix}{{P\left\{ \; {\; = \hat{x}} \right\}} = \left\{ {\begin{matrix}{2 \cdot {\int_{0}^{{({1 - f})} \cdot Q_{step}}{{f_{uv}^{\prime}(x)}{dx}}}} & {\hat{x} = 0} \\{\int_{{({{\hat{x}} - f})} \cdot Q_{step}}^{{({{\hat{x}} + 1 - f})} \cdot Q_{step}}{{f_{uv}^{\prime}(x)}{dx}}} & {\hat{x} \neq 0}\end{matrix} = \left\{ {\begin{matrix}{{2{\left( {1 - f} \right) \cdot Q_{step} \cdot {f_{uv}^{\prime}\left( x^{*} \right)}}\hat{x}} = 0} \\{{{Q_{step} \cdot {f_{uv}^{\prime}\left( x^{*} \right)}}\hat{x}} \neq 0}\end{matrix};} \right.} \right.} & \left( {{equation}\mspace{14mu} 4} \right)\end{matrix}$

where f represents quantization offset, and Q_(step) representsquantization step size. In the case of {circumflex over (x)}≠0,x*=|{circumflex over (x)}|·Q_(step) is taken as an approximate result ofthe computation, while in the case of {circumflex over (x)}=0, it isunnecessary to compute the probability; as mentioned below, the numberof coding bits in the case of the quantization result being 0 isneglected.

A computation expression for the uniform distribution in the model isrepresented in equation 5:

P{

={circumflex over (x)}}=Q _(step) ·f _(uv)′(b _(uv))  (equation 5)

After obtaining the probability, the number of coding bits may bepredicted through the equation 6 using an information entropy theory.

$\begin{matrix}{r_{uv} = \left\{ {\begin{matrix}0 & {\hat{x} = 0} \\{{- \log_{2}}\left\{ \; {\; = \hat{x}} \right\}} & {\hat{x} \neq 0}\end{matrix};} \right.} & \left( {{equation}\mspace{14mu} 6} \right)\end{matrix}$

In this way, the number of coding bits for each prediction block may beobtained through equation 7:

r _(B)=Σ_(u)Σ_(v) r _(uv)  (equation 7)

It needs to be noted that the equation for probability computation has arelatively high computation complexity. The computation result may besaved in a lookup table such that it is only needed to compute once whenupdating the probability model and then cache the result; in this way,the result may be directly searched in the subsequent RDO module,thereby effectively enhancing the time efficiency of the RDO module.

The scheme above may quickly predict an optimal prediction mode in thesame block size; however, this predicted number of coding bits does nothave a desired effect in deciding with respect to different block sizes,such that it is needed to correct the predicted code rate.

During the procedure of correcting the predicted code rate,specifically, after an optimal mode for one block size is obtained, thismode may be subjected to a simple entropy coding once, and the result isused as the basis for RDO decision between different block sizes. Theprinciple of the simple entropy coding is relatively simple. It is onlyneeded to binarize the complete entropy coding procedure and then usethe binarized number of bits as the final predicted number of codingbits.

Now, all steps of predicting the code rate during the RDO process arecompleted.

It needs to be noted that the embodiments as disclosed are intended tofacilitating further understanding of the present disclosure; however,those skilled in the art may understand that various substitutions andmodifications are possible without departing from the spirit and scopeof the present disclosure. Therefore, the present disclosure should notbe limited to the contents disclosed in the embodiments, but should begoverned by the appended claims.

I/We claim:
 1. An intra code-rate predicting method in video coding,which enables skipping of an entropy coding procedure during arate-distribution optimization process and effectively reduces a codingtime by modeling residual information of a prediction block andpredicting the number of coding bits based on a model resulting from themodeling; the intra code-rate predicting method comprises: a procedureof making statistics on prediction block distribution information andmodeling, a procedure of predicting the number of coding bits of eachprediction mode during a rate distortion optimization process based onthe model, and a procedure of correcting predicted number of codingbits; wherein 1) the procedure of making statistics on prediction blockdistribution information and modeling comprises: 11) partitioning theprediction block into different sizes, making statistics, in a unit ofone frame, on distribution of residuals at each position in differentsizes, and fitting the residuals at each position in the predictionblock using a combined model that combines generalized Gaussiandistribution and uniform distribution model, wherein a resultingprobability distribution function is used as a combined model of aprobability distribution at each position in the prediction block, 12)computing a quantization value at each position of the prediction blockand a probability of the quantization value to be a specific value; thensolving an information entropy based on the probability, where theinformation entropy is used as the predicted number of coding bitscorresponding to the quantization value of a corresponding position ofthe prediction block; and saving the predicted number of coding bits ina data table; 2) the procedure of predicting the number of coding bitsof each prediction mode during the rate distortion optimization processbased on the model comprises: during the rate distortion optimizationprocess, after the procedures of predicting, transforming, andquantizing, obtaining the predicted number of coding bits for theposition by looking up the data table from step 12) based on thequantization result at each position, and obtaining the predicted numberof coding bits of the prediction block by summing up numbers ofpredicted coding bits at all positions of the prediction block, therebyskipping the entropy coding procedure; and 3) the procedure ofcorrecting the predicted number of coding bits comprises: performing asimple entropy coding for an optimal prediction mode for a currentprediction block, and using a result of the simple entropy coding resultas the predicted number of coding bits for an optimal mode, for singlingout a best partitioning mode between different block sizes; therebycompleting the intra code-rate prediction during the rate distortionoptimization process.
 2. The intra code-rate predicting method accordingto claim 1, wherein in the step 11) of making statistics on theprediction blocks of different sizes, the different sizes range from4×4˜64×64.
 3. The intra code-rate predicting method according to claim1, wherein in the step 11), the generalized Gaussian distribution modeland the uniform distribution model are combined into a combined model,the combined model being expressed as: $\begin{matrix}{{f_{uv}^{\prime}(x)} = \left\{ {\begin{matrix}{{{\theta_{uv} \cdot {f_{uv}(x)}}{x}} \leq b_{uv}} \\{{{f_{uv}^{\prime}\left( b_{uv} \right)}b_{uv}} < {x} \leq m_{uv}} \\\begin{matrix}{0\mspace{34mu}} & {{x} \geq m_{uv}}\end{matrix}\end{matrix};} \right.} & \left( {{equation}\mspace{14mu} 3} \right)\end{matrix}$ where θ_(uv) is an adjustment factor to guarantee that anintegral of a probability density function is 1 in an entire interval;b_(uv) represents a boundary between the generalized Gaussiandistribution and the uniform distribution; m_(uv) represents anavailable maximum value of x after quantization; and f_(uv)(x)represents a generalized Gaussian distribution function, with anexpression being equation 1: $\begin{matrix}{{{{f_{uv}(x)} = {\frac{\eta_{uv}{\alpha_{uv}\left( \eta_{uv} \right)}}{2\sigma_{uv}{\Gamma \left( {1/\eta_{uv}} \right)}}\exp \left\{ {- \left\lbrack {{\alpha_{uv}\left( \eta_{uv} \right)}\frac{x}{\sigma_{uv}}} \right\rbrack^{\eta_{uv}}} \right\}}};}{{wherein}\text{:}}} & \left( {{equation}\mspace{14mu} 1} \right) \\{{{\alpha_{uv}\left( \eta_{uv} \right)} = \sqrt{\frac{\Gamma \left( {3/\eta_{uv}} \right)}{\Gamma \left( {1/\eta_{uv}} \right)}}};} & \left( {{equation}\mspace{14mu} 2} \right)\end{matrix}$ and wherein in the expression, f_(uv)(x) represents aprobability density distribution) of each position in the predictionblock; u and v are coordinates of a position in the prediction block;σ_(uv) denotes a standard deviation at the position; η_(uv) controls ashape of the probability density function, being predicted using anexpression of${\eta_{uv} = {{0.2718/\left( {0.7697 - \frac{E^{2}\left\{ {x} \right\}}{E\left\{ x^{2} \right\}}} \right)} - 0.1247}};$and Γ(⋅) represents a gamma function.
 4. The intra code-rate predictingmethod according to claim 1, wherein the step 2) of predicting thenumber of coding bits of each prediction mode based on the combinedmodel specifically comprises steps of: 21) first, computing aprobability of the quantization result to be a certain value based onthe probability density function; a computation expression for thegeneralized Gaussian distribution in the model being provided inequation 4: $\begin{matrix}{{P\left\{ \; {\; = \hat{x}} \right\}} = \left\{ {\begin{matrix}{2 \cdot {\int_{0}^{{({1 - f})} \cdot Q_{step}}{{f_{uv}^{\prime}(x)}{dx}}}} & {\hat{x} = 0} \\{\int_{{({{\hat{x}} - f})} \cdot Q_{step}}^{{({{\hat{x}} + 1 - f})} \cdot Q_{step}}{{f_{uv}^{\prime}(x)}{dx}}} & {\hat{x} \neq 0}\end{matrix} = \left\{ {\begin{matrix}{{2{\left( {1 - f} \right) \cdot Q_{step} \cdot {f_{uv}^{\prime}\left( x^{*} \right)}}\hat{x}} = 0} \\{{{Q_{step} \cdot {f_{uv}^{\prime}\left( x^{*} \right)}}\hat{x}} \neq 0}\end{matrix};} \right.} \right.} & \left( {{equation}\mspace{14mu} 4} \right)\end{matrix}$ where f represents a quantization offset, Q_(step)represents a quantization step size; if {circumflex over (x)}≠0,x′=|{circumflex over (x)}|·Q_(step) is taken as an approximate result ofthe computation; if {circumflex over (x)}=0, the probability is notcomputed, and if the quantization result being 0 the number of codingbits is neglected; and a computation expression for the uniformdistribution in the model being represented in equation 5:P{

={circumflex over (x)}}=Q _(step) ·f _(uv)′(b _(uv))  (equation 5) 22)after obtaining the probability of the quantization result to be acertain value, predicting a number of coding bits r_(uv) throughequation 6: $\begin{matrix}{r_{uv} = \left\{ {\begin{matrix}0 & {\hat{x} = 0} \\{{- \log_{2}}\left\{ \; {\; = \hat{x}} \right\}} & {\hat{x} \neq 0}\end{matrix};} \right.} & \left( {{equation}\mspace{14mu} 6} \right)\end{matrix}$ and 23) obtaining a number of coding bits r_(B) for eachprediction block through equation 7:r _(B)=Σ_(u)Σ_(v) r _(uv)  (equation 7); where Q_(step) represents thequantization step size; f_(uv)′(b_(uv)) represents the combined model;and b_(uv) represents a boundary between the generalized Gaussiandistribution and the uniform distribution.
 5. The intra code-ratepredicting method according to claim 1, wherein the simple entropycoding in step 3) including binarizing an entire entropy coding processand using a binarized number of bits as an entropy coding result.